The subject matter disclosed herein relates to analysis of image data and analysis to classify multiple objects into one or more object types in image data. More specifically, the present techniques relate to unsupervised, automated object classification.
In one example, the subject matter disclosed herein relates to image analysis of a biological sample. A given tissue microenvironment may include cells of distinct lineage and function. Better classification of the cellular composition and attendant phenotype of individual cells in the tissue microenvironment in healthy and disease states may advance basic, translational, and clinical research and ultimately improve human health. Pathologists strive for accurate and reproducible recognition of cellular patterns in tissue samples, with the cell counting and classification being one of the major factors affecting their assessments of a given sample. Cell counting typically involves obtaining a biological sample, preparing it for imaging (typically, a step involving staining), collecting images on a microscope, and then determining information based off recognizable cellular patterns.
However, cell counting is intrinsically difficult due to various issues (non-specific staining, overlapping markers, dense clusters of objects, artifacts, etc.). One approach to select out relevant objects that were successfully expressed via manual annotation. Such annotation involves building recognition models to assist with cell counting/differentiation across large data sets. However, the annotation task is laborious, time-consuming, and has potential for individual bias.